This course will be run on-line via zoom, except for the final exam which is onsite (but local examination might be arranged for PhD students at other universities).

Upon completion of the course, the doctoral student will be able to: explain the basic theoretical foundation and practical use of Structural Equation Models (SEM), apply SEM on real problems, interpret and present the results, estimate structural equation models with maximum likelihood and least squares and be able to evaluate the results.

A Ph.D. course in modern statistical inference theory for students in statistics, mathematical statistics, and related areas. Two onsite meetings (Lund and Stockholm) and three online meetings. The details about the course can be found at https://krys.neocities.org/Teaching/StatInf/PhD_stat_inf.html.

We will discuss algorithms to compute minima or maxima which are frequently needed in statistics and machine learning. Topics of lectures on four occasions: gradient based algorithms, stochastic gradient based algorithms, gradient free algorithms (e.g. particle swarm optimisation), handling of restrictions during optimisation. Course homepage: http://www.adoptdesign.de/optimisation1.html

All lectures are online via Zoom. The lecturer is Prof. Dr. Claudia Czado (https://www.professoren.tum.de/en/czado-claudia/) who also wrote the course book. Info regarding the course is available in the attached course plan. The course will take place in the last week of august (detailed schedule is attached). Any questions contact Kristofer Månsson (kristofer.mansson@ju.se).

Due to the outbreak of the coronavirus all lectures will be online via Zoom.The lecturer is Dr. Abdul Aziz Ali. Info regarding the course is available in the attached course plan. The course will start in the midle of may. Any questions contact Kristofer Månsson (kristofer.mansson@ju.se).

At the Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University, 14th-19th May 2020 we will be hosting a school concerning stochastic differential equations and the YUIMA R package (Simulation and Inference for SDEs and Other Stochastic Processes, https://cran.r-project.org/web/packages/yuima/index.html). The lectures will be given by members of the YUIMA team ( https://yuimaproject.com/ ).

This is an advanced course in Bayesian statistics for PhD students in statistics, computer science, the engineering sciences and other related fields.
The course is divided into 3-5 contemporary topics in Bayesian analysis, and the choice of topics can vary from year to year depending on the research frontier.
The planned topics for the current year are (preliminary and subject to change):

1. Gaussian Processes with Applications
2. Bayesian Nonparametrics
3. (Stochastic) Variational Inference
4. Bayesian Model Inference

All lectures are expected to be streamed live online, through Zoom. Apart from traditional white-board lectures, there will also be computer classes to ensure a strong connection to empirical econometric modelling.  
We will be following the book Econometric analysis: 8th Edition. W. H. Greene closely throughout the course. The exam will consist of a number of home assignment involving theoretical matters as well as empirical analyses. Students who have not yet applied to the course should do this asap, directly to the course coordinator (see contact info below). 

During the fall 2019 professor Richard Davis, Columbia University, will give a course on Topics in Time Series Analysis: Old to New.

The course is aimed at advanced masters students and PhD students from Chalmers and Gothenburg University, and also welcomes students from other Scandinavian universities. The first meeting will be 

Monday, September 23, 13:15-15:00, room MVH 12 in the Mathematics Building, Chalmers tvärgata 3